Deep Learning Bootcamp

A two-day bootcamp on learning Deep Learning

9-16 Sep 2017, Bangalore

Approach

This would be a two-day instructor-led hands-on workshop to learn and implement an end-to-end deep learning model for computer vision (image recognition and generation) and natural language processing (text classfication and generation)

Day 1 will cover introduction to deep learning and applications to computer vision

Day 2 will cover applications to natural language processing

There will be eight sessions in total of two hours each over two days.

Session 1: Deep Learning (DL) Theory

What is deep learning?

Use cases in computer vision and natural language processing.

Overview of the building blocks

Neurons

Activation functions

Back propagation algorithm

Stochastic gradient descent

Adaptive learning

Momentum

Session 2: DL for Computer Vision

Introduction to problem and data-set

Working on the cloud, including keras and tensorflow

Build your first DL Model - Multi-layer Perceptron (MLP)

Session 3: Convolutional Neural Networks (CNN)

Concept of Convolution, Max-pooling and Dropouts

Build your second DL Model - CNN

Tricks to improve your model

Augment your training data

Batch normalization

Session 4: Transfer Learning

Concept of Transfer Learning

Build your third DL Model - Leverage pre-trained models

Deploying your DL model on the cloud

Session 5: DL for Natural Language Processing (NLP)

Challenges with traditional NLP techniques

Concept of Word Embedding - word2vec

Build your fourth DL Model - MLP using word2vec

Session 6: Recurrent Neural Networks (RNN)

Concept of RNNs

Concept of Long Short-Term Memory (LSTM)

Build your fifth DL Model - LSTM

Session 7: Build your DL Applications

Concept of Sequence-to-Sequence Learning

Build your sixth DL Model - Text Generation

Deploy it as a bot (e.g. TweetBot / ChatBot)

Session 8: Advanced Topics in DL (Theory)

Challenges in building DL apps

Concept of Generative Adversarial Network

Moving beyond Classification e.g. Object Detection

Concept of DL for Unsupervised Learning

Concept of Reinforcement Learning

Where to go from here

“All knowledge is connected to all other knowledge. The fun is in making the connections.” - Arthur Aufderheide

The objective for the Deep Learning bootcamp is to ensure that the participants have enough theory and practical concepts of building a deep learning solution in the space of computer vision and natural language processing. Post the bootcamp, all the participants would be familiar with the following key concepts and would be able to apply them to a problem.

Resources

This is from the popular workshop series by the speakers on deep learning. Additional materials relevant to learning Deep Learning would be shared prior to the workshop.

Target Audience

A machine learning practitioner

A programmer interested in building data science products

Anyone (researcher, student, professional) learning machine learning

Corporates and Start-ups looking to add DL to their product or service offerings

Pre-requisites

This is a hands-on course and hence, participants should be comfortable with programming. Familiarity with python data stack is ideal.

Prior knowledge of machine learning will be helpful. Participants should have some practice with basic machine learning problems e.g. regression, classification.

While the DL concepts will be taught in an intuitive way, some prior knowledge of linear algebra and calculus would be helpful.

Software Requirements

We will be using Python data stack for the workshop with keras and tensorflow for the deep learning component. Please install Ananconda for Python 3 for the workshop. Additional requirement will be communicated to participants.

Instructors

Amit teaches the craft of telling visual stories with data. He conducts workshops and trainings on Data Science in Python and R, as well as on Data Visualisation topics. His background is in strategy consulting having worked with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps.

Bargava is a practicing Data Scientist. He has 14 years of experience delivering business analytics solutions to Investment Banks, Entertainment Studios and High-Tech companies. He has given talks and conducted workshops on Data Science, Machine Learning, Deep Learning and Optimization in Python and R. He has a Masters in Statistics from University of Maryland, College Park, USA. He is an ardent NBA fan.

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